الرئيسية الأخبار أحدث الأوراق البحثية الدروس مجموعات البيانات الموسوعة SOTA نماذج LLM لوحة الأداء GPU الفعاليات

العربية

Anomaly Detection On One Class Cifar 100

المقاييس

AUROC

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	AUROC	Paper Title	Repository
GeneralAD	98.4	GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features
Transformaly	97.7	Transformaly -- Two (Feature Spaces) Are Better Than One
DisAug CLR	86.5	Learning and Evaluating Representations for Deep One-class Classification
PANDA-OE	97.3	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Self-Supervised Multi-Head RotNet	80.1	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
GAN based Anomaly Detection in Imbalance Problems	87.4	GAN-based Anomaly Detection in Imbalance Problems	-
DUIAD	86	Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework	-
Geom	78.7	Deep Anomaly Detection Using Geometric Transformations
Self-Supervised One-class SVM, RBF kernel	62.6	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Self-Supervised DeepSVDD	67	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Mean-Shifted Contrastive Loss	96.5	Mean-Shifted Contrastive Loss for Anomaly Detection
Rotation Prediction	84.1	Learning and Evaluating Representations for Deep One-class Classification
MTL	83.95	Shifting Transformation Learning for Out-of-Distribution Detection	-
CSI	89.6	CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
PANDA	94.1	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

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